Courses

An introduction to discrete mathematics covering proof techniques, recursion and structural induction, recurrences, functions and relations, graphs and trees, finite automata and regular expressions, and counting.

One of the more rigorous linear algebra offerings at UW–Madison. We began with abstract properties of vector spaces, subspaces, and fields, then covered matrix computation, eigenvalues, diagonalization, Gram–Schmidt, and orthogonal complements. Notes in whuang369/MATH-341.

My first formal CS course at UW–Madison. Covered Java fundamentals (objects, polymorphism) and elementary data structures (BST, heap). Ten Java programming assignments.

Compilation, basic digital logic, and assembly programming in LC-3.

The final course in the Java sequence. Practical tooling (Linux, git) alongside algorithms (Kruskal, Dijkstra) and data structures (red-black trees).

Introduction to real analysis: basic set theory, topology on metric spaces, formal definitions of derivative and integral, and properties of sequences, series, and functions. Textbook: Principles of Mathematical Analysis by Rudin (formerly of UW–Madison).

Abstract algebra: basic set theory, group theory (with group actions), ring theory, Euclidean domains, PIDs, and congruence problems via CRT. Mostly followed Prof. Chenxi Wu's lecture notes. Related blog posts.

Point-set topology: set theory, topology on metric spaces, then general topology. Related blog posts.

Permutations and combinations, binomial coefficients, inclusion–exclusion, recurrences, generating functions, special counting sequences (Stirling, Catalan), and Pólya counting. Textbook by Prof. Brualdi (UW–Madison).

Broad survey of classical AI topics; see the course page for the full syllabus. Ten Python programming assignments. Solid probability and linear algebra are essential prerequisites in practice.

Pedagogy seminar on CS education: use of LLMs, fostering belonging in CS courses, and the qualities of effective peer mentors. Included structured observations of TAs and peer mentors.

Taught by Prof. Dieter van Melkebeek. Greedy, dynamic programming, divide-and-conquer, network flow, P/NP/NP-hard/NP-complete, reductions, and randomness. Weekly graded problems plus optional practice; two midterms and one final. Strongly recommend the honors section.

Taught by Prof. Yong Jae Lee. CNNs, R-CNN, RNNs, optimization, object detection, segmentation, attention, vision transformers, VAEs, GANs, diffusion, and self-supervised learning.

Rudin Ch. 7–9: uniform convergence, Stone–Weierstrass, Fourier series, the Gamma function, the inverse and implicit function theorems, and the rank theorem. The honors track additionally covered Lebesgue theory.

University string orchestra open to all students. I play cello. Weekly rehearsals with a concert at the end of each semester.